31 lines
2.5 KiB
Markdown
31 lines
2.5 KiB
Markdown
---
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layout: single
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title: "Emergent Social Dynamics"
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categories: research
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tags: artificial-life complex-systems neural-networks self-organization emergent-behavior predictive-coding
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excerpt: "Artificial chemistry networks develop predictive models via surprise minimization."
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header:
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teaser: /assets/figures/18_surprised_soup_teaser.jpg
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scholar_link: "https://scholar.google.de/citations?user=NODAd94AAAAJ&hl=en"
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---
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This research extends the study of **artificial chemistry** systems populated by neural network "particles," focusing on the emergence of complex behaviors driven by **social interaction** rather than explicit programming. Building on systems where particles may exhibit self-replication, we introduce interactions based on principles of **predictive processing and surprise minimization** (akin to the Free Energy Principle).
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{:style="display:block; width:40%" .align-right}
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Specifically, particles are equipped with mechanisms enabling them to **recognize and build predictive models of their peers' behavior**. The learning process is driven by the minimization of prediction error, or "surprise," incentivizing particles to accurately anticipate the actions or state changes of others within the "soup."
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Key observations from this setup include:
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* The emergence of **stable behavioral patterns and population dynamics** purely from these local, predictive interactions. Notably, these emergent patterns often resemble the stability observed in systems where self-replication was an explicitly trained objective.
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* The introduction of a unique **"catalyst" particle** designed to exert evolutionary pressure on the system, demonstrating how external influences or specialized agents can shape the collective dynamics.
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<center>
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<img src="/assets/figures/18_surprised_soup_trajec.jpg" alt="Trajectories or state space visualization of the particle population dynamics over time" style="display:block; width:90%">
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<figcaption>Visualization of particle trajectories or population dynamics within the 'social soup'.</figcaption>
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</center>
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This study highlights how complex, seemingly goal-directed social behaviors and stable ecosystem structures can emerge from simple, local rules based on mutual prediction and surprise minimization among interacting agents, offering insights into the self-organization of complex adaptive systems. {% cite zorn23surprise %}
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